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README.md

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 # Lightning Rod Labs
 
-**Generate training data from real-world sources.**
+**Train with Timestamps, Not Labels.**
 
-Go from messy historical data to verified training datasets — no labeling or annotation needed.
-Define your criteria in Python, and our SDK treats real-world outcomes as the label, producing high-signal supervision at scale. Models learn causal factors, not just tokens. Raw data to deployable specialized models in hours.
+Lightning Rod Labs automatically generates high-quality training data from your documents or public sources — no labeling or extraction required. Define your criteria in Python, and our SDK treats real-world outcomes as the label, producing high-signal supervision at scale. Models learn causal factors, not just tokens. Raw data to deployable specialized models in hours.
 
 [Website](https://lightningrod.ai/) · [SDK](https://github.com/lightning-rod-labs/lightningrod-python-sdk) · [Blog](https://blog.lightningrod.ai/)
 
 ---
 
+## How It Works
+
+We generate grounded, model-ready training data from documents or public sources (Google News, SEC filings, market data). You define your criteria in Python, and our SDK uses the **future as the label** — turning messy, timestamped history into training signal automatically. No labeling pipelines, no extraction, no human annotation.
+
+This approach has been used to beat frontier AIs 100x larger on prediction-market benchmarks, and has demonstrated success in financial forecasting, risk estimation, and policy prediction.
+
+---
+
 ## Research & Results
 
 - **[SEC Risk Prediction](https://arxiv.org/abs/2601.19189)**: Foresight learning on raw SEC filings trains a 32B model to outperform GPT-5 at predicting public company risks.
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 - **[Foresight-32B vs. Frontier LLMs](https://blog.lightningrod.ai/p/foresight-32b-beats-frontier-llms-on-live-polymarket-predictions)**: Live demonstration beating frontier models on Polymarket predictions.
 
 Foresight-32B is consistently top-ranked on [ForecastBench](https://www.forecastbench.org/tournament/) and [ProphetArena Sports](https://www.prophetarena.co/leaderboard).
+
+---
+
+## Models & Datasets
+
+| Resource | Description |
+|----------|-------------|
+| [Trump-Forecaster](https://huggingface.co/LightningRodLabs/Trump-Forecaster) | RL-tuned gpt-oss-120b LoRA adapter for predicting Trump administration actions. Beats GPT-5 (Brier 0.194 vs 0.200). |
+| [Golf-Forecaster](https://huggingface.co/LightningRodLabs/Golf-Forecaster) | RL-tuned gpt-oss-120b LoRA adapter for predicting professional golf outcomes. Beats GPT-5.1 (Brier 0.207 vs 0.218). |
+| [WWTD-2025](https://huggingface.co/datasets/LightningRodLabs/WWTD-2025) | 2,790 binary forecasting questions about U.S. policy under the Trump administration, with news context and ground-truth resolutions. |
+| [GolfForecasting](https://huggingface.co/datasets/LightningRodLabs/GolfForecasting) | 4,033 binary forecasting questions about professional golf across PGA Tour, LIV Golf, LPGA, and majors. |